Global land cover data are key
sources of information for understanding the complex interactions between human
activities and global change. FROM-GLC (Finer Resolution Observation and
Monitoring of Global Land Cover) is the first 30 m resolution global land cover
maps produced using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper
Plus (ETM+) data. Our long-term goal in FROM-GLC is to develop a multiple stage
approach to mapping global land cover so that the results can better meet the
needs of land process modeling and can be easily cross-walked to existing global
land cover classification schemes.

Classification system

Level 1 Type

Level 1 Coce

Level 2 Type

Level 1/2 Code

Level 2 Type

Level 1/2 Code

Level 2 Type

Level 1/2 Code

Level 2 Type

Level 1/2 Code

Level 2 Type

Level 1/2 Code

Level 2 Type

Level 1/2 Code

Crop

10

Rice

10/11

Greenhouse

10/12

Other

10/13

Forest

20

Broadleaf

20/21

Needleleaf

20/22

Mixed

20/23

Orchard

20/24

Grass

30

Managed

30/31

Nature

30/32

Shrub

40

Wetland

50

Grass

30/51

Silt

90/52

Water

60

Lake

60/61

Pond

60/62

River

60/63

Sea

60/64

Tundra

70

Shrub

40/71

Grass

30/72

Impervious

80

High albedo

80/81

Low albedo

80/82

Bareland

90

Saline-Alkali

90/91

Sand

90/92

Gravel

90/93

Bare-cropland

10/94

Dry river/lake bed

90/95

other

90/96

Snow/Ice

100

Snow

100/101

Ice

100/102

Cloud

120

Legend

Land cover type (Level1)

Level 1 Code

Level 1 Color

R Value

G Value

B Value

Background

0

0

0

Cropland

10

163

255

115

Forest

20

38

115

0

Grass

30

76

230

0

Shrub

40

112

168

0

Water

60

0

92

255

Impervious

80

197

0

255

Bareland

90

255

170

0

Snow/Ice

100

0

255

197

Cloud

120

255

255

255

Methods

[1] FROM-GLCFROM-GLC (Gong et
al., 2013) was produced using 91433 training samples and 38664 test samples
collected via human interpretation of TM/ETM+ images. The interpretation was
carried out based primarily on a color composite of TM images of Bands 4, 3, and
2 displayed with the red, green and blue color guns respectively. In addition,
the spectral curve based on the 6 optical bands of TM/ETM+, the MODIS time
series during the whole year of 2010, and high resolution images and field
photos found in Google Earth were used as reference.Four sets of global land
cover maps were produced based respectively on four types of supervised
classifiers including the conventional maximum likelihood classifier (MLC), the
J4.8 decision tree classifier, the random forests ensemble classifier (RF) and
the support vector machine (SVM). The SVM produced the highest overall
classification accuracy of approximately 64.9% that was assessed with a set of
test samples independently collected. The random forests produced the second
highest classification accuracy of 59.8%, with J4.8 and the MLC ranked the third
to the fourth.

[2] FROM-GLC-segFROM-GLC-seg
(Yu et al., 2013) is an improved version of FROM-GLC. A segmentation approach
was used in FROM-GLC-seg to integrate multi-resolution datasets, including
Landsat TM/ETM+ (30 meter), MODIS EVI time series (250 meter), Bioclimatic
variables (1km) (Hijmans et al., 2005), global DEM (1km) (Hijmans et al., 2005),
Soil-water variables (1km) (Zomer et al., 2007; 2008; Trabucco & Zomer,
2010). FROM-GLC-seg used the same training/test samples as FROM-GLC, and
followed the same classification system with slight modification (The impervious
land cover type was not mapped, due to severe spectral mixing effects and its
small coverage. In addition, the clouds, which temporally exist on Landsat
TM/ETM+, were removed as well). The Random Forest (RF) classifier was used and
achieved better overall accuracy. Accuracies for vegetation land cover types
(i.e. cropland, forest) and bareland were improved. However, mapping accuracies
for water bodies, snow/ice land cover types are slightly lower because coarser
resolution MODIS (250 meter) and Bioclimatic, DEM, Soil-Water variables (1km)
are not ideal for recognizing small scale objects.

[3] FROM-GLC-aggFROM-GLC-agg
(Yu et al., 2014) is a further improvement by aggregating FROM-GLC and
FROM-GLC-seg, together with two coarse resolution impervious maps, i.e.
Nighttime Light Impervious Surface Area (Elvidge et al., 2007) and MODIS urban
extent (Schneider et al., 2009; 2010). FROM-GLC-agg has an overall accuracy of
65.51%, which is significantly better than FROM-GLC (63.69%) and FROM-GLC-seg
(64.42%). Accuracies for individual land cover types in FROM-GLC-agg have been
increased or better balanced compared to FROM-GLC and FROM-GLC-seg.

[4] FROM-GCFROM-GC (Yu et
al., 2013) is a 30-m spatial resolution global cropland extent (with other land
cover types) product developed with two 30-m global land cover maps (i.e.
FROM-GLC, Finer Resolution Observation and Monitoring, Global Land Cover;
FROM-GLC-agg) and a 250-m cropland probability map (Pittman et al., 2010). A
common land cover validation sample database (Zhao et al., 2014) was used to
determine optimal thresholds of cropland probability in different parts of the
world to generate a cropland/noncropland mask according to the classification
accuracies for cropland samples. A decision tree was then applied to combine two
250-m cropland masks: one existing mask from the literature and the other
produced in this study, with the 30-m global land cover map FROM-GLC-agg. For
the smallest difference with country-level cropland area in Food and Agriculture
Organization Corporate Statistical (FAOSTAT) database, a final global cropland
extent map was composited from the FROM-GLC, FROM-GLC-agg, and two masked
cropland layers. From this map FROM-GC (Global Cropland), we estimated the
global cropland areas to be 1533.83 million hectares (Mha) in 2010, which is
6.95 Mha (0.45%) less than the area reported by the Food and Agriculture
Organization (FAO) of the United Nations for the year 2010. A country-by-country
comparison between the map and the FAOSTAT data showed a linear relationship
(FROM-GC = 1.05*FAOSTAT ?1.2 (Mha) with R2=?0.97). Africa, South America,
Southeastern Asia, and Oceania are the regions with large discrepancies with the
FAO survey.